Fast 3D point cloud segmentation using supervoxels with geometry and color for 3D scene understanding

Francesco Verdoja, Diego Thomas, Akihiro Sugimoto

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

28 Sitaatiot (Scopus)

Abstrakti

Segmentation of 3D colored point clouds is a research field with renewed interest thanks to recent availability of inexpensive consumer RGB-D cameras and its importance as an unavoidable low-level step in many robotic applications. However, 3D data's nature makes the task challenging and, thus, many different techniques are being proposed, all of which require expensive computational costs. This paper presents a novel fast method for 3D colored point cloud segmentation. It starts with supervoxel partitioning of the cloud, i.e., an oversegmentation of the points in the cloud. Then it leverages on a novel metric exploiting both geometry and color to iteratively merge the supervoxels to obtain a 3D segmentation where the hierarchical structure of partitions is maintained. The algorithm also presents computational complexity linear to the size of the input. Experimental results over two publicly available datasets demonstrate that our proposed method outperforms state-of-the-art techniques.

AlkuperäiskieliEnglanti
OtsikkoIEEE International Conference on Multimedia and Expo (ICME 2017)
JulkaisupaikkaHong Kong
Sivut1285-1290
Sivumäärä6
ISBN (elektroninen)9781509060672
DOI - pysyväislinkit
TilaJulkaistu - 28 elokuuta 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Multimedia and Expo - Hong Kong, Hongkong
Kesto: 10 heinäkuuta 201714 heinäkuuta 2017

Conference

ConferenceIEEE International Conference on Multimedia and Expo
LyhennettäICME
MaaHongkong
KaupunkiHong Kong
Ajanjakso10/07/201714/07/2017

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